Factors Affecting Long-Term Trends in Global NDVI

Abstract

Diagnosing the evolution trends of vegetation and its drivers is necessary for ecological conservation and restoration. However, it remains unclear what the underlying distribution pattern of these trends and its correlation with some drivers at large spatial-temporal scales. Here we use the normalized difference vegetation index (NDVI) to quantify the activity of vegetation by Theil–Sen median trend analysis and the Mann–Kendall test, Pearson correlation analysis and Boosted regression trees (BRT) model. Results show that about 34% of the global continent area has experienced greening in the grid annual NDVI from 1982 to 2015. The major greening areas were observed in the Sahel, European, India and south China. Only 10% of the global continent land areas were browning, and these were observed in Canada, South America, central Africa and Central Asia. BRT model shows that rainfall is the most important factor affecting vegetation evolution (63.1%), followed by temperature (15%), land cover change (8.6%), population (6.5%), elevation (6.4%) and nightlight (0.4%). It’s about 21% of the world’s continent were affected by rainfall, mainly in arid regions such as central Asia and Australia. The main temperature-affected areas accounted for 36%, located near the equator or in high latitudes.

Influence of Variations of Hydrothermal Conditions on Normalized Difference Vegetation Index in Typical Temperature Zones Along the East Coast of China

“…The monthly NDVI in Guangdong, Jiangsu, and Liaoning showed an overall upward trend during the growing season from 1982 to 2016. This result was consistent with the research results of the NDVI change trends on different spatial scales such as global (Yang et al, 2019), Eurasia (Piao et al, 2011), China (Peng et al, 2011), and eastern China (Jiang et al, 2014). The change rates of the NDVI in the three regions showed a law of increasing from north to south.…”

Evaluation of temporal and spatial changes of global ecosystem health

“…The calculation of both the indicators is based on land use and is largely subject to land use change. In addition, in spatial terms, indicators of ecosystem health show significant differences, and global NDVI is significantly affected by climate factors (Munavar, Carsten, Christian, Dietrich, & Nicole, 2018; Peng, Kuang, & Tao, 2019; Yang et al, 2019; Zheng et al, 2018); as the climate changes, so does the global EV.…”

“…For a long time, many scholars have used Pearson correlation analysis to study the relationship between variables (Li et al, 2020; Yang et al, 2019), the determination of the relationship between EHI and influencing factors is mainly accomplished by calculating and verifying the correlation coefficients (Li et al, 2020; Zhang, Feng, Jiang, & Yang, 2015). The formula is presented as follows: Where: n is the number of samples; and are the means of variables x and y , respectively; and R xy is the correlation coefficient between variables x and y .…”

Change Detection of Soil Formation Rate in Space and Time Based on Multi Source Data and Geospatial Analysis Techniques

“…The NDVI data of the Global Inventory Modeling and Mapping Studies represent a vegetation product obtained by the AVHRR sensor mounted on a NOAA satellite [37,38]. The dataset had a spatial resolution of 8 km × 8 km (0.083 • ) and composite values of 15 days from 1982 to present [39]. The data were accessible online at https://ecocast.arc.nasa.gov/data/pub/gimms/.…”

“…We selected hydrochemical data from 43 river basins in the database to calculate the ionic activity coefficients of calcium and bicarbonate ions ( Figure 2). The multiyear average data were from 1996 to 2012. spatial resolution of 8 km × 8 km (0.083°) and composite values of 15 days from 1982 to present [39]. The data were accessible online at https://ecocast.arc.nasa.gov/data/pub/gimms/.…”